[1] Merkle, D., Middendorf, M., & Schmeck, H. (2002). Ant colony optimization for resource-constrained project scheduling. IEEE transactions on evolutionary computation, 6(4), 333-346.
[2] Zhang, H., Li, H., & Tam, C. M. (2006). Particle swarm optimization for resource-constrained project scheduling. International journal of project management, 24(1), 83-92.
[3] Zhang, H., Li, X., Li, H., & Huang, F. (2005). Particle swarm optimization-based schemes for resource-constrained project scheduling. Automation in construction, 14(3), 393-404.
[4] Rogalska, M., Bożejko, W., & Hejducki, Z. (2008). Time/cost optimization using hybrid evolutionary algorithm in construction project scheduling. Automation in Construction, 18(1), 24-31.
[5] Balouka, N., & Cohen, I. (2021). A robust optimization approach for the multi-mode resource-constrained project scheduling problem. European journal of operational research, 291(2), 457-470.
[6] Wang, L., & Zheng, X. L. (2018). A knowledge-guided multi-objective fruit fly optimization algorithm for the multi-skill resource constrained project scheduling problem. Swarm and Evolutionary Computation, 38, 54-63.
[7] Guo, K., & Zhang, L. (2022). Multi-objective optimization for improved project management: Current status and future directions. Automation in Construction, 139, 104256.
[8] Xie, L. L., Chen, Y., Wu, S., Chang, R. D., & Han, Y. (2024). Knowledge extraction for solving resource-constrained project scheduling problem through decision tree. Engineering, Construction and Architectural Management, 31(7), 2852-2877.
[9] Yu, Ze, Chuxin Wang, Yuanyuan Zhao, Zhiyuan Hu, and Yuanjie Tang. "Linear Project-Scheduling Optimization Considering a Reverse Construction Scenario." Applied Sciences 13, no. 16 (2023): 9407.
[10] Zohrehvandi, M., Zohrehvandi, S., Khalilzadeh, M., Amiri, M., Jolai, F., Zavadskas, E. K., & Antucheviciene, J. (2024). A Multi-Objective Mathematical Programming Model for Project-Scheduling Optimization Considering Customer Satisfaction in Construction Projects. Mathematics, 12(2), 211.
[11] Ashrafian, A., Behnood, A., Golafshani, E. M., Panahi, E., & Berenjian, J. (2024). Toward presenting an ensemble meta‐model for evaluation of pozzolanic mixtures incorporating industrial by‐products. Structural Concrete, 25(2),
[12] Fan, Su-Ling, Kuo-Shun Sun, and Yu-Ren Wang. "GA optimization model for repetitive projects with soft logic." Automation in Construction 21 (2012): 253-261.
[13] Pawiński, Grzegorz, and Krzysztof Sapiecha. "Resource allocation optimization in critical chain method." Annales UMCS, Informatica. Vol. 12. No. 1. (2012).
[14] Singh, Amol. "Resource constrained multi-project scheduling with priority rules & analytic hierarchy process." Procedia Engineering 69 (2014): 725-734.
[15] Peng, L., and P. Wuliang. "An Efficient Simulation Algorithm for Resource-Constrained Project Scheduling Problem." Open Mechanical Engineering Journal 8 (2014): 9-13.
[16] Abdolshah, Mohammad. "A Review of Resource-Constrained Project Scheduling Problems (RCPSP) Approaches and Solutions." (2014).
[17] Ali, Ismail M., et al. "A Differential Evolution Algorithm for Solving Resource Constrained Project Scheduling Problems." Australasian Conference on Artificial Life and Computational Intelligence. Springer International Publishing, (2016).
[18] B. Khang, A. Ming, H. genetic algorithm for mode identity and the resource constrained project scheduling problem, Sci. Iran. 20 (3) (2017) 824–831.
[19] H. Zhang, C. Tam, H. Li, Multi-mode project scheduling based on particle swarm optimization, Comput. Aided Civ. Infrastruct. Eng. 21 (201) 93–103.
[20] A. Tavana, S. Colak, S. Erenguc, A neurogenetic approach for the resource constrained project scheduling problem, Comput. Oper. Res. 38 (1) (2019) 44–50.
[21] Lazari, V., Chassiakos, A., & Karatzas, S. (2024). Multi-objective resource-constrained scheduling in large and repetitive construction projects. Algorithms, 17(8), 351.
https://doi.org/10.3390/a17080351
[22] Zhou, J., Tang, Y., & Tian, Y. (2025). Multi-Objective Trade-Offs for Construction Projects with Dual Constraints of Schedule Risk and Resources Under a Risk-Driven Perspective. Sustainability, 17(5), 1926.
[23] Zhang, H., Li, H., & Tam, C. M. (2012). Impact of quality management on project cost performance. Construction Management and Economics, 30(7), 565-578.
[24] Khamooshi, H., & Golafshani, H. (2014). EDM: Earned duration management, a new approach to schedule performance management and measurement. International Journal of Project Management, 32(6), 1019-1041.
[25] Assaf, S., & Hassanain, M. (2019). Causes of quality failures in building construction projects. Journal of Building Engineering, 20, 101-112.
[26] Shao, X., Li, P., & Ding, L. (2020). A multi-objective optimization model for construction project scheduling considering quality, time and cost. Automation in Construction, 113, 103138
[27] Marini, F., & Walczak, B. (2015). Particle swarm optimization (PSO). A tutorial. Chemometrics and Intelligent Laboratory Systems, 149, 153-165.
[28] Ishibuchi, H., Imada, R., Setoguchi, Y., & Nojima, Y. (2016, July). Performance comparison of NSGA-II and NSGA-III on various many-objective test problems. In 2016 IEEE Congress on Evolutionary Computation (CEC) (pp. 3045-3052). IEEE.
[29] Zhang, L., Du, J., & Zhang, S. (2014). Solution to the time-cost-quality trade-off problem in construction projects based on immune genetic particle swarm optimization. Journal of Management in Engineering, 30(2), 163-172.
[30] Monghasemi, S., Nikoo, M. R., Fasaee, M. A. K., & Adamowski, J. (2015). A novel multi criteria decision making model for optimizing time–cost–quality trade-off problems in construction projects. Expert systems with applications, 42(6), 3089-3104.
[31] Teymourifar, A., Rodrigues, A. M., & Ferreira, J. S. (2020, July). A comparison between NSGA-II and NSGA-III to solve multi-objective sectorization problems based on statistical parameter tuning. In 2020 24th International Conference on Circuits, Systems, Communications and Computers (CSCC) (pp. 64-74). IEEE.
[32] Gu, Q., Xu, Q., & Li, X. (2022). An improved NSGA-III algorithm based on distance dominance relation for many-objective optimization. Expert Systems with Applications, 207, 117738.
[33] Sedaghat Shayegan D. Optimum cost design of reinforced concrete slabs using a metaheuristic algorithm, Int J Optim Civil Eng 2022; 12(4): 545-55.
[34] Saberi AA, Sedaghat Shayegan D (2021) Optimization of Haraz Dam reservoir operation using CBO metaheuristic algorithm. Journal Int J Optim Civ Eng 11(4):599–610. https:// doi. org/ 10. 13140/RG.2. 2. 14648. 85636
[35] Saberi AA, Ahmadi H, Sedaghat Shayegan D, Amirkardoust A (2023) Prediction of electricity consumption using three meta-heuristic algorithms. Int J Optim Civil Eng. 13(1):111–125
[36] Sedaghat Shayegan D, Lork A, Hashemi AH. optimum cost design of reinforced concrete slabs using Mouth Brooding Fish (MBF) algorithm, J Appl Eng Sci 2020; 10(23): ISSUE 1 ART.NO. 290: pp. 95-100.
[37] Bhesdadiya, R. H., Trivedi, I. N., Jangir, P., Jangir, N., & Kumar, A. (2016). An NSGA-III algorithm for solving multi-objective economic/environmental dispatch problem. Cogent Engineering, 3(1), 1269383.
[38] Zhang, H., & Xing, F. (2010). Fuzzy-multi-objective particle swarm optimization for time–cost–quality tradeoff in construction. Automation in Construction, 19(8), 1067-1075.